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Abstract Determining the optimum location of wells during waterflooding contributes significantly to efficient reservoir management. Often, Voidage Replacement Ratio (VRR) and Net Present Value (NPV) are used as indicators of performance of waterflood projects. In addition, VRR is used by regulatory and environmental agencies as a means of monitoring the impact of field development activities on the environment while NPV is used by investors as a measure of profitability of oil and gas projects. Over the years, well placement optimization has been done mainly to increase the NPV. However, regulatory measures call for operators to maintain a VRR of one (or close to one) during waterflooding. A multiobjective approach incorporating NPV and VRR is proposed for solving the well placement optimization problem. We present the use of both NPV and VRR as objective functions in the determination of optimal location of wells. The combination of these two in a multiobjective optimization framework proves to be useful in identifying the trade-offs between the quest for high profitability of investment in oil and gas projects and the desire to satisfy regulatory and environmental requirements. We conducted the search for optimum well locations in three phases. In the first phase, only the NPV was used as the objective function. The second phase has the VRR as the sole objective function. In the third phase, the objective function was a weighted sum of the NPV and the VRR. A set of four weights were used in the third phase to describe the relative importance of the NPV and the VRR and a comparison of how these weights affect the optimized NPV and VRR values is provided. We applied the method to determine the optimum placement of wells using two sample reservoirs: one with a distributed permeability field and the other, a channel reservoir with four facies. Two evolutionary-type algorithms: the covariance matrix adaptation evolutionary strategy (CMA-ES) and differential evolution (DE), were used to solve the optimization problem. Significantly, the method illustrates the trade-off between maximizing the NPV and optimizing the VRR. It calls the attention of both investors and regulatory agencies to the need to consider the financial aspect (NPV) and the environmental aspect (VRR) of waterflooding during secondary oil recovery projects. The multiobjective optimization approach meets the economic needs of investors and the regulatory requirements of government and environmental agencies. This approach gives a realistic NPV estimation for companies operating in jurisdiction with requirement for meeting a VRR of one.
- Asia > Middle East (0.46)
- North America > United States (0.46)
- Europe > Austria (0.28)
Summary The steam-assisted gravity-drainage (SAGD) process is used widely to recover heavy oil and bitumen from formations in which no other recovery method has proved to be economical. It is an energy-intensive process, and because of economic and environmental reasons, solvents as additives to the injected steam are being explored currently to reduce the energy and emissions intensity of SAGD. The solvent-aided process (SAP), tested in the field and described in the literature, is one such attempt. In the SAP, a small amount of hydrocarbon solvent is introduced as an additive to the injected steam. Thus, the viscosity of the oil is also reduced because of solvent dilution in addition to heating. The SAP can improve the energy efficiency of SAGD significantly, thus reducing the heat requirement, as shown in field trials discussed elsewhere. However, on the use of the right amount of solvent that can result in best overall performance, there is very little discussion in the literature. Because of the high cost of such solvents, there is incentive to optimize their use in SAGD. Recently, various authors have attempted to address the subject with, for example, arbitrary time-dependent schemes of solvent injections, assessing their impact on results or by treating the internal reservoir dynamics as a black box and using optimization methods, such as genetic algorithms (GAs), to estimate the optimal amount of solvent. While these approaches orient us to the problem in a context-specific manner, it is believed a generalized treatment to estimate optimal use of solvent requires a mechanism-based understanding. The approach presented in this paper is aimed at estimating the optimal solvent in the context of SAGD. It combines the existing Butler's oil-drainage analytical models (Butler 1985, 1988, 1994) for SAGD and vapor extraction (VAPEX), which deal with heating effect and solvent-dilution effect one at a time, into one. Then, it calculates the time-dependent steam rates to maintain the predicted oil rates in conjuction with solvent rates and, thus, estimates the solvent/steam ratio (SSR) and the steam/oil ratio (SOR). The results are discussed for a few light-alkane solvents. In the process of this exercise, it is discovered that to obtain reasonable SSR and SOR, a significant amount of oil has to drain from a diffuse layer, which has a varying temperature, solvent concentration, and gas saturation (from maximaum gas saturation at the injection end to zero at the vapor/liquid interface).
- Materials > Chemicals > Commodity Chemicals > Petrochemicals (1.00)
- Energy > Oil & Gas > Upstream (1.00)
Summary Numerous steam-assisted gravity-drainage (SAGD) optimization studies published in the literature combined numerical simulation with graphical or analytical techniques for design and performance evaluation. Efforts have integrated the simulation exercise with global optimization algorithms. Some studies focused on optimization of cumulative steam/oil ratio (cSOR) in SAGD by altering steam-injection rates, while others focused on optimization of net cumulative energy/oil ratio (cEOR) in solvent-additive SAGD by altering injection pressures and fraction of solvent in the injection stream. Several studies also considered total project net-present-value (NPV) calculation by changing total project area, capital-cost intensities, solvent prices, and risk factors to determine the well spacing and drilling schedule. Optimization techniques commonly used in those studies were scattered search, simulated annealing, and genetic algorithm (GA). However, applications of hybrid GA were rarely found. In this paper, we focused on optimization of solvent-assisted SAGD using various GA implementations. In our models, hexane was selected to be coinjected with steam. The objective function, defined on the basis of cSOR and recovery factor, was optimized by changing injection pressures, production pressures, and injected solvent/steam ratio. Techniques, including orthogonal arrays (OA) for experimental design (e.g., Taguchi's arrays) and proxy models for objective-function (F) evaluations, were incorporated with the GA method to improve computational and convergence efficiency. Results from these hybrid approaches revealed that an optimized solution could be achieved with less central-processing-unit time (e.g., fewer number of iterations) compared with the conventional GA method. Sensitivity analysis was also conducted on the choice of proxy model to study the robustness of the proposed methods. To investigate the effects of heterogeneity in the design process, optimization of solvent-assisted SAGD was performed on various synthetic heterogeneous reservoir models of porosity, permeability, and shale distributions. Our results highlight the potential application of the proposed techniques in other solvent-enhanced heavy-oil-recovery processes.
- Asia > Middle East > Saudi Arabia (0.46)
- North America > Canada > Alberta (0.30)
- North America > United States > California (0.28)
- Materials > Chemicals > Commodity Chemicals > Petrochemicals (1.00)
- Energy > Oil & Gas > Upstream (1.00)
Abstract This paper presents a new approach developed for high-level scoping analysis, forecasting and scheduling of CO2 EOR projects for multiple reservoirs and fields. The approach utilizes available reservoir simulation, analytical predictions and analog data on a full-field scale and approximates them with analytical functions. This allows for very fast forecasting of oil and CO2 production rates and determines the requirements for make-up CO2 under different potential development scenarios, including piloting phases. Built in Microsoft Excel with VBA code and an advanced solver add-in, this scheduling tool enables the timely use of probabilistic Monte Carlo simulation for estimating the impact of uncertain input parameters on CO2 flood performance from multiple reservoirs. A numerical optimization algorithm searches for the best development schedule by optimizing the start-up times for a number of planned CO2 injection projects subject to allowable oil rate and CO2 supply constraints. Another optimization algorithm matches the estimated CO2 demand with supply from multiple natural and industrial sources and predicts the best time to commission CO2 capture facilities, thus maximizing CO2 utilization by EOR schemes rather than disposing it in depleted reservoirs or saline aquifers.
- North America > United States > Texas (1.00)
- Europe (0.68)
- Asia > Middle East > UAE > Abu Dhabi Emirate > Abu Dhabi (0.16)
- North America > United States > Texas > Permian Basin > Yeso Formation (0.99)
- North America > United States > Texas > Permian Basin > Yates Formation (0.99)
- North America > United States > Texas > Permian Basin > Wolfcamp Formation (0.99)
- (22 more...)
Abstract Molecular diffusion effect has been ignored in many conventional reservoir studies, but it can play a significant role in tight fractured reservoirs and is crucial for an appropriate reservoir evaluation. We have characterized diffusion coefficients of methane gas in hydrocarbon fluid samples by combining experiment and simulation. We employ the theory of Fickian diffusive flux to evaluate molecular diffusion behavior. It is described as a product of concentration gradient, molar density and Fickian diffusion coefficient. Diffusion coefficients, although commonly estimated by existing correlations, shall be calibrated against actual molecular diffusion behavior for practical use. There have been few published papers showing Fickian multicomponent diffusion coefficients, because a simple and reliable measurement method has not been available. We hereby propose a method for computer-assisted multicomponent diffusion coefficient evaluation based on experiments proposed by Riazi that uses a PVT cell apparatus. We measure molecular diffusion behavior in a PVT cell and evaluate diffusion coefficients using Leahy-Dios and Firoozabadi's model with the assumption of local equilibrium described by the Peng-Robinson EOS. Volume shift parameters, binary interaction coefficients, initial pressure, initial liquid volume and diffusion coefficients are optimized to fit measurements by a new global optimization algorithm named iterative Latin hypercube samplings. Simulation case studies are performed to show the effect of molecular diffusion in tight fractured reservoirs. Results indicate that injected gas diffuses deeply into rock matrices when diffusive flux is properly represented. Consequently, better sweep efficiency is achieved compared to the cases without considering diffusion. In this paper, we present a systematic method to evaluate diffusion coefficients of reservoir fluids using EOS. It will be useful for reservoir simulation of oil and gas recovery in tight fractured reservoirs.
- North America > United States (0.28)
- North America > Canada > Alberta (0.28)
Copyright 2012, Society of Petroleum Engineers This paper was prepared for presentation at the SPE Russian Oil & Gas Exploration & Production Technical Conference and Exhibition held in Moscow, Russia, 16-18 October 2012. This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright. Abstract In petroleum exploration, reservoir navigation is used for reaching a productive reservoir and placing the borehole optimally inside the reservoir to maximize production. For proper well placement, it is necessary to calculate in real-time parameters of the formation we are drilling in, and the parameters of formations we are approaching. Based on these results, a decision to change the direction of drilling could be made. Modern logging while drilling (LWD) extra-deep and azimuthal resistivity tools acquire multi-component, multi-spacing, and multi-frequency data that provide sufficient information for resolving the surrounding formation parameters. These tools are generally used for reservoir navigation and real-time formation evaluation. However, real-time interpretation software very often is based on simplified resistivity models that can be inadequate and lead to incorrect geosteering decisions. The core of the newly developed software is an inversion algorithm based on a model of transversely-isotropic layered earth with an arbitrary number of layers. The following model parameters are determined in real time: horizontal and vertical resistivities and thickness of each layer, formation dip, and azimuth. The inversion algorithm is based on the method of the most-probable parameter combination. The algorithm has good performance and excellent convergence due to its enhanced capability of avoiding local minima.
- North America > United States (0.46)
- Europe > Russia > Central Federal District > Moscow Oblast > Moscow (0.25)
- Geology > Geological Subdiscipline > Stratigraphy (0.48)
- Geology > Rock Type > Sedimentary Rock > Clastic Rock > Mudrock > Shale (0.32)
Abstract Setting proper water injection rates for the injection wells is a key factor to successfully operate an oil field under water flooding. The success of such activity could (a) reduce water cycling at field, section and pattern levels; (b) improve water/oil ratio (WOR) and areal sweep efficiency; (c) improve oil production and recovery by directing water injection to specific zones and areas; and (d) reduce OPEX by improving water utilization. Typically, the onsite engineers adjust injection rates using heuristics. While this does improve performance we feel that a more systematic approach can be developed which will lead to further gains. In this paper, we present a systematic method, using the linear programming, to optimize the water injection target rates. In this method, the reservoir is considered to be a system which can be modeled as a collection of continuous-time impulse responses that convert injection rates into a production rate. A very simple two parameter parametric model, like diffusivity-filter, has been used to quantify the injector-producer continuous-time impulse responses channel model and the Extended Kalman Filter has been used to establish the allocation factors between injectors and producers in the water-flooded field. Subject to constraints, including the total available water amount, the maximum and minimum injection rates, the maximum total production fluid for a producer and a gauge setting, a linear programming optimizer has been applied to determine the optimized water injection rate, based on the established allocation factors. This method was pilot tested on a Chevron oilfield for 3 months. The decline curve for 6 months and for 2 months of historical oil production data have been calculated. The 3 month pilot test result indicated that the optimized oil production matches the historical 6-month decline curve very well with about 22% less total daily water injection. Also we saw about 2% incremental production above the historical 2- month decline curve (again with about 22% less total daily water injection). These results suggest that this systematic method may provide a way to optimize the water injection target rates.
- Water & Waste Management > Water Management > Lifecycle > Disposal/Injection (1.00)
- Energy > Oil & Gas > Upstream (1.00)
Integrated Asset Modeling for Reservoir Management of a Miscible WAG Development on Alaska's Western North Slope
Roadifer, R. D. (ConocoPhillips Alaska Inc) | Sauvé, R.. (Schlumberger) | Torrens, R.. (Schlumberger Middle East SA) | Mead, H. W. (ConocoPhillips Alaska Inc) | Pysz, N. P. (ConocoPhillips Alaska Inc) | Uldrich, D. O. (ConocoPhillips Alaska Inc) | Eiben, T.. (ConocoPhillips Canada)
Abstract An integrated asset modeling (IAM) approach has been implemented for the Alpine Field and eight associated satellite fields on the Western Alaskan North Slope (WNS) to maximize asset value and recovery. The IAM approach enables the investigation of reservoir and facilities management options under existing and future operating constraints. Oil, gas and water production from these fields are processed at the Alpine Central Facility (ACF). A number of local constraints exist for the asset, such as the requirement that all associated gas be used for facilities power generation, gas lift or re-injection. All produced water must be re-injected and, for pipeline integrity reasons, must be segregated from imported make-up sea water used for injection. Additionally, surface gas and water handling capacity is limited at the ACF. To further complicate matters, gas injected for EOR purposes is enriched such that it is miscible or near-miscible at reservoir conditions. These conditions create a unique and changing relationship between the oil, gas and water production, gas lift, miscible water alternating gas (MWAG) injection, lean gas injection, facilities constraints and injection availability. The scope of the current IAM project has been multi-fold. Optimization of oil production across all WNS fields requires the placement of injection fluids be simultaneously optimized. The optimization procedure begins by allocation of oil production targets based on current operating conditions, the potentials of the wells in each field to deliver fluids, and total gas lift availability. Excess gas compression capacity is utilized for gas lift and is allocated via an incremental gas-oil ratio sort on the production wells. Given the constraints on water injection noted above, optimization of injection fluids begins by determining pump requirements for produced water and the optimal field or injection manifold placement of the produced water. Following this, optimized placement of the miscible injectant (MI) and lean gas injectant (LGI) is determined based on a dynamic MWAG scheduling methodology developed to maximize oil recovery and ensure the number of gas injection wells have sufficient capacity to inject the required volume of gas in each reservoir. The volumetric split of gas into MI and LGI streams falls out directly from the specification of a target minimum miscibility pressure (MMP) constraint for the MI and the volume of condensates driven off the top of the condensate stabilizer column at the process facility. Finally, the volume of the make-up fluid (sea water) is determined based on the minimum of the remaining pump capacity or potential of the remaining wells to inject the water and allocated to each field based on a fractional oil voidage replacement scheme. Maximizing production across multiple fields necessarily requires that the best player (well) plays, regardless of the field to which it belongs. This requirement relates to both instantaneous production as would be considered under a gas lift optimization scenario as well as the longer term MWAG performance and recovery of each individual well pattern across all the fields. The IAM technology utilized for managing the WNS fields consists of full-field compositional reservoir simulation models for each reservoir integrated with a pipeline surface network model and a process facility model. Spreadsheet based allocation routines and advanced mathematical coupling algorithms complete the IAM model enabling not only the prediction of the assets’ performance under the aforementioned constraints, capacities and operating conditions, but to optimize overall performance and analyze the impact of decisions. To the authors’ knowledge, this is the first time integrated asset modeling has been applied to bring the entire production stream including reservoir, wellbore, surface network and process simulation together for planning and managing MWAG injection to optimize recovery from an existing development.
- North America > United States > Texas (0.92)
- North America > United States > Alaska > North Slope Borough (0.88)
- Energy > Oil & Gas > Upstream (1.00)
- Water & Waste Management > Water Management > Lifecycle > Disposal/Injection (0.36)
- North America > United States > Alaska > North Slope Basin > Kuparuk River Field (0.99)
- Asia > Middle East > Israel > Tel Aviv District > Southern Levant Basin > National Field (0.97)
- North America > United States > Alaska > North Slope Basin > Western North Slope > Colville River Field > Alpine Field > Kingak Formation (0.94)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery > Miscible methods (1.00)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery > Gas-injection methods (1.00)
- Reservoir Description and Dynamics > Fluid Characterization > Fluid modeling, equations of state (1.00)
- (2 more...)
Abstract The increasing demand of crude oil has led to the increasing needs to improve recovery. Waterflooding is one of the most common secondary recovery techniques applied to many reservoirs. The method of waterflooding is to inject water into the reservoir to maintain reservoir pressure. In general, the determination of well locations is structured to form certain pattern (e.g. five spot). However, it is not the only factor affecting the optimum configuration. Besides reservoir rocks, fluid characteristics and well configurations, injection and production strategies (e.g. rates or bottom hole pressures) can also significantly affecting the overall recovery. As reported by other researchers, due to the complex nature of reservoir characterizations and fluid properties, the optimization solution of those parameters tends to have many best possible solutions. Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) were chosen because their robustness to avoid local optima and finding the global optima of an objective function. The objective function used to drive the solutions were Recovery Factor (RF) and Net Present Value (NPV) of a waterflooding project. An Optimization tool, (OpTool) was developed to prove that stochastic optimization methods could fit best in optimizing the waterflooding project. This tool is capable to communicate with reservoir simulators with known input/output formats to populate cases that would be evaluated using the well-defined objective functions. A synthetic case was built to show the OpTool capabilities to tackle waterflooding optimization problems. This tool is capable to determine an optimum pattern design (e.g. well spacing) and production and injection strategy to maximizing the RF and NPV of the project.
Abstract The success of any green as well as mature field development planning and execution lies in the ability to recreate the environment of deposition with the appropriate spatial and temporal facies interdependencies. This, when done with proper application of the varied suite of geotechnical software/knowhow, often leads to the creation of a finite number of high resolution and equiprobable reservoir models within a macro and micro sedimentological framework, that readily lends itself to optimized risk and uncertainty management. This becomes even more critical in deepwater turbidite systems where the impact of geologic uncertainties can significantly reduce project value and does often prevent marginal field developments in the absence of a low cost tie-in option. This paper presents the novel application of one such technique; the QuantiMin methodology approach improves aspects of reservoir characterization and facilitates various aspects of well and reservoir management in the waterflood development of a Miocene deepwater turbidite system in the Gulf of Guinea. The QuantiMin technique is a sequential quadratic program that solves non-linear problems by series of quadratic programming steps. When applied in this context, it assesses the mineral and fluid content around the near wellbore area, based on their unique well log responses, and returns with a finite volume distribution of mineral and fluid distribution around the wellbore, using the mineral and fluid distribution input from appraisal well cores as the calibration or control variable. The results from QuantiMin analysis have been used in this field to: Evaluate the potential impact of mineralogy on the performance of water injection wells. Apply understanding of mineral types around the wellbore to the design of acid stimulation recipes. Develop a framework for understanding the field scale distribution of heterogeneities by establishing the interdependencies between log-scale QuantiMin and microscopic core petrography data, and hence facilitate high resolution reservoir characterization. Establish realistic flow potentials for development wells.
- Geology > Sedimentary Geology > Depositional Environment > Marine Environment > Deep Water Marine Environment (1.00)
- Geology > Mineral (1.00)
- Africa > Middle East > Libya > Murzuq District > Murzuq Basin > Block NC 186 > Field A Field > Silurian Tanezzuft Formation (0.97)
- Africa > Middle East > Libya > Murzuq District > Murzuq Basin > Block NC 115 > Field A Field > Silurian Tanezzuft Formation (0.97)
- Reservoir Description and Dynamics > Reservoir Characterization (1.00)
- Reservoir Description and Dynamics > Improved and Enhanced Recovery > Waterflooding (1.00)
- Reservoir Description and Dynamics > Formation Evaluation & Management > Open hole/cased hole log analysis (1.00)
- Management > Asset and Portfolio Management > Field development optimization and planning (1.00)